Tracing the transition of methicillin resistance in sub-populations of Staphylococcus aureus, using SELDI-TOF Mass Spectrometry and Artificial Neural Network Analysis

Strains ( n = 99) of Staphylococcus aureus isolated from a large number of clinical sources and tested for methicillin sensitivity were analysed by MALDI-TOF-MS using the Weak Cation Exchange (CM10) ProteinChip Array (designated SELDI-TOF-MS). The profile data generated was analysed using Artificial...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Systematic and applied microbiology 2011-02, Vol.34 (1), p.81-86
Hauptverfasser: Shah, Haroun N., Rajakaruna, Lakshani, Ball, Graham, Misra, Raju, Al-Shahib, Ali, Fang, Min, Gharbia, Saheer E.
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 86
container_issue 1
container_start_page 81
container_title Systematic and applied microbiology
container_volume 34
creator Shah, Haroun N.
Rajakaruna, Lakshani
Ball, Graham
Misra, Raju
Al-Shahib, Ali
Fang, Min
Gharbia, Saheer E.
description Strains ( n = 99) of Staphylococcus aureus isolated from a large number of clinical sources and tested for methicillin sensitivity were analysed by MALDI-TOF-MS using the Weak Cation Exchange (CM10) ProteinChip Array (designated SELDI-TOF-MS). The profile data generated was analysed using Artificial Neural Network (ANN) Analysis modelling techniques. Seven key ions identified by the ANNs that were predictive of MRSA and MSSA were validated by incorporation into a model. This model exhibited an area under the ROC curve value of 0.9147 indicating the potential application of this approach for rapidly characterising MRSA and MSSA isolates. Nearly all strains ( n = 97) were correctly assigned to the correct group, with only two aberrant MSSA strains being misclassified. However, approximately 21% of the strains appeared to be in a process of transition as resistance to methicillin was being acquired.
doi_str_mv 10.1016/j.syapm.2010.11.002
format Article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_907152616</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0723202010001645</els_id><sourcerecordid>853225957</sourcerecordid><originalsourceid>FETCH-LOGICAL-c414t-8e796c27243802a0d12b92bbfedb4029d690e4e7a032eca9c43c9e07297545783</originalsourceid><addsrcrecordid>eNqFkc9u1DAQhyMEokvhCZDANy5ksZ0_jg8cVqWFSgs97PZsOc6k6yWJU48DygvxnDjdwhFOI1vf_MaeL0leM7pmlJUfjmuc9divOV1u2JpS_iRZsZJVKZVV_jRZUcGzlFNOz5IXiEdKWS5L9jw544wXggu5Sn7tvTZ2uCPhACR4PaAN1g3EtaSHcLDGdp0diAe0GPRggMQTTnU6unHq9MLiAu-CHg9z54wzZkKiJw8TvicTLtm7y-2n63R_c0W-akSyG8EE72K-n4keGrLxwbZxlO7IN5j8Qwk_nf9ONoPu5jj6ZfKs1R3Cq8d6ntxeXe4vvqTbm8_XF5ttanKWh7QCIUvDBc-zinJNG8Zryeu6habOKZdNKSnkIDTNOBgtTZ4ZCXFNUhR5IarsPHl3yh29u58Ag-otGug6PYCbUEkqWMFLVv6XrIqM80IWIpLZiTTeIXpo1ehtr_2sGFWLSXVUDybVYlIxpqLJ2PXmMX-qe2j-9vxRF4G3J6DVTuk7b1Hd7mJCGTWLMqfLZz6eCIgb-2HBKzQWosPG-mhANc7-8wm_ARwPvAs</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>853225957</pqid></control><display><type>article</type><title>Tracing the transition of methicillin resistance in sub-populations of Staphylococcus aureus, using SELDI-TOF Mass Spectrometry and Artificial Neural Network Analysis</title><source>MEDLINE</source><source>Elsevier ScienceDirect Journals Complete</source><creator>Shah, Haroun N. ; Rajakaruna, Lakshani ; Ball, Graham ; Misra, Raju ; Al-Shahib, Ali ; Fang, Min ; Gharbia, Saheer E.</creator><creatorcontrib>Shah, Haroun N. ; Rajakaruna, Lakshani ; Ball, Graham ; Misra, Raju ; Al-Shahib, Ali ; Fang, Min ; Gharbia, Saheer E.</creatorcontrib><description>Strains ( n = 99) of Staphylococcus aureus isolated from a large number of clinical sources and tested for methicillin sensitivity were analysed by MALDI-TOF-MS using the Weak Cation Exchange (CM10) ProteinChip Array (designated SELDI-TOF-MS). The profile data generated was analysed using Artificial Neural Network (ANN) Analysis modelling techniques. Seven key ions identified by the ANNs that were predictive of MRSA and MSSA were validated by incorporation into a model. This model exhibited an area under the ROC curve value of 0.9147 indicating the potential application of this approach for rapidly characterising MRSA and MSSA isolates. Nearly all strains ( n = 97) were correctly assigned to the correct group, with only two aberrant MSSA strains being misclassified. However, approximately 21% of the strains appeared to be in a process of transition as resistance to methicillin was being acquired.</description><identifier>ISSN: 0723-2020</identifier><identifier>EISSN: 1618-0984</identifier><identifier>DOI: 10.1016/j.syapm.2010.11.002</identifier><identifier>PMID: 21257279</identifier><language>eng</language><publisher>Germany: Elsevier GmbH</publisher><subject>ANN ; Bacterial Proteins - analysis ; CM10 ; Drug Resistance, Bacterial ; Methicillin-Resistant Staphylococcus aureus - chemistry ; Methicillin-Resistant Staphylococcus aureus - classification ; MRSA ; MSSA ; Neural Networks (Computer) ; Protein Array Analysis - methods ; SELDI-TOF-MS ; Sensitivity and Specificity ; Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization - methods ; Staphylococcus aureus</subject><ispartof>Systematic and applied microbiology, 2011-02, Vol.34 (1), p.81-86</ispartof><rights>2010 Elsevier GmbH</rights><rights>Copyright © 2010 Elsevier GmbH. All rights reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c414t-8e796c27243802a0d12b92bbfedb4029d690e4e7a032eca9c43c9e07297545783</citedby><cites>FETCH-LOGICAL-c414t-8e796c27243802a0d12b92bbfedb4029d690e4e7a032eca9c43c9e07297545783</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.syapm.2010.11.002$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/21257279$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Shah, Haroun N.</creatorcontrib><creatorcontrib>Rajakaruna, Lakshani</creatorcontrib><creatorcontrib>Ball, Graham</creatorcontrib><creatorcontrib>Misra, Raju</creatorcontrib><creatorcontrib>Al-Shahib, Ali</creatorcontrib><creatorcontrib>Fang, Min</creatorcontrib><creatorcontrib>Gharbia, Saheer E.</creatorcontrib><title>Tracing the transition of methicillin resistance in sub-populations of Staphylococcus aureus, using SELDI-TOF Mass Spectrometry and Artificial Neural Network Analysis</title><title>Systematic and applied microbiology</title><addtitle>Syst Appl Microbiol</addtitle><description>Strains ( n = 99) of Staphylococcus aureus isolated from a large number of clinical sources and tested for methicillin sensitivity were analysed by MALDI-TOF-MS using the Weak Cation Exchange (CM10) ProteinChip Array (designated SELDI-TOF-MS). The profile data generated was analysed using Artificial Neural Network (ANN) Analysis modelling techniques. Seven key ions identified by the ANNs that were predictive of MRSA and MSSA were validated by incorporation into a model. This model exhibited an area under the ROC curve value of 0.9147 indicating the potential application of this approach for rapidly characterising MRSA and MSSA isolates. Nearly all strains ( n = 97) were correctly assigned to the correct group, with only two aberrant MSSA strains being misclassified. However, approximately 21% of the strains appeared to be in a process of transition as resistance to methicillin was being acquired.</description><subject>ANN</subject><subject>Bacterial Proteins - analysis</subject><subject>CM10</subject><subject>Drug Resistance, Bacterial</subject><subject>Methicillin-Resistant Staphylococcus aureus - chemistry</subject><subject>Methicillin-Resistant Staphylococcus aureus - classification</subject><subject>MRSA</subject><subject>MSSA</subject><subject>Neural Networks (Computer)</subject><subject>Protein Array Analysis - methods</subject><subject>SELDI-TOF-MS</subject><subject>Sensitivity and Specificity</subject><subject>Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization - methods</subject><subject>Staphylococcus aureus</subject><issn>0723-2020</issn><issn>1618-0984</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2011</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqFkc9u1DAQhyMEokvhCZDANy5ksZ0_jg8cVqWFSgs97PZsOc6k6yWJU48DygvxnDjdwhFOI1vf_MaeL0leM7pmlJUfjmuc9divOV1u2JpS_iRZsZJVKZVV_jRZUcGzlFNOz5IXiEdKWS5L9jw544wXggu5Sn7tvTZ2uCPhACR4PaAN1g3EtaSHcLDGdp0diAe0GPRggMQTTnU6unHq9MLiAu-CHg9z54wzZkKiJw8TvicTLtm7y-2n63R_c0W-akSyG8EE72K-n4keGrLxwbZxlO7IN5j8Qwk_nf9ONoPu5jj6ZfKs1R3Cq8d6ntxeXe4vvqTbm8_XF5ttanKWh7QCIUvDBc-zinJNG8Zryeu6habOKZdNKSnkIDTNOBgtTZ4ZCXFNUhR5IarsPHl3yh29u58Ag-otGug6PYCbUEkqWMFLVv6XrIqM80IWIpLZiTTeIXpo1ehtr_2sGFWLSXVUDybVYlIxpqLJ2PXmMX-qe2j-9vxRF4G3J6DVTuk7b1Hd7mJCGTWLMqfLZz6eCIgb-2HBKzQWosPG-mhANc7-8wm_ARwPvAs</recordid><startdate>20110201</startdate><enddate>20110201</enddate><creator>Shah, Haroun N.</creator><creator>Rajakaruna, Lakshani</creator><creator>Ball, Graham</creator><creator>Misra, Raju</creator><creator>Al-Shahib, Ali</creator><creator>Fang, Min</creator><creator>Gharbia, Saheer E.</creator><general>Elsevier GmbH</general><scope>FBQ</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>7QL</scope><scope>C1K</scope></search><sort><creationdate>20110201</creationdate><title>Tracing the transition of methicillin resistance in sub-populations of Staphylococcus aureus, using SELDI-TOF Mass Spectrometry and Artificial Neural Network Analysis</title><author>Shah, Haroun N. ; Rajakaruna, Lakshani ; Ball, Graham ; Misra, Raju ; Al-Shahib, Ali ; Fang, Min ; Gharbia, Saheer E.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c414t-8e796c27243802a0d12b92bbfedb4029d690e4e7a032eca9c43c9e07297545783</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2011</creationdate><topic>ANN</topic><topic>Bacterial Proteins - analysis</topic><topic>CM10</topic><topic>Drug Resistance, Bacterial</topic><topic>Methicillin-Resistant Staphylococcus aureus - chemistry</topic><topic>Methicillin-Resistant Staphylococcus aureus - classification</topic><topic>MRSA</topic><topic>MSSA</topic><topic>Neural Networks (Computer)</topic><topic>Protein Array Analysis - methods</topic><topic>SELDI-TOF-MS</topic><topic>Sensitivity and Specificity</topic><topic>Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization - methods</topic><topic>Staphylococcus aureus</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Shah, Haroun N.</creatorcontrib><creatorcontrib>Rajakaruna, Lakshani</creatorcontrib><creatorcontrib>Ball, Graham</creatorcontrib><creatorcontrib>Misra, Raju</creatorcontrib><creatorcontrib>Al-Shahib, Ali</creatorcontrib><creatorcontrib>Fang, Min</creatorcontrib><creatorcontrib>Gharbia, Saheer E.</creatorcontrib><collection>AGRIS</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Environmental Sciences and Pollution Management</collection><jtitle>Systematic and applied microbiology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Shah, Haroun N.</au><au>Rajakaruna, Lakshani</au><au>Ball, Graham</au><au>Misra, Raju</au><au>Al-Shahib, Ali</au><au>Fang, Min</au><au>Gharbia, Saheer E.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Tracing the transition of methicillin resistance in sub-populations of Staphylococcus aureus, using SELDI-TOF Mass Spectrometry and Artificial Neural Network Analysis</atitle><jtitle>Systematic and applied microbiology</jtitle><addtitle>Syst Appl Microbiol</addtitle><date>2011-02-01</date><risdate>2011</risdate><volume>34</volume><issue>1</issue><spage>81</spage><epage>86</epage><pages>81-86</pages><issn>0723-2020</issn><eissn>1618-0984</eissn><abstract>Strains ( n = 99) of Staphylococcus aureus isolated from a large number of clinical sources and tested for methicillin sensitivity were analysed by MALDI-TOF-MS using the Weak Cation Exchange (CM10) ProteinChip Array (designated SELDI-TOF-MS). The profile data generated was analysed using Artificial Neural Network (ANN) Analysis modelling techniques. Seven key ions identified by the ANNs that were predictive of MRSA and MSSA were validated by incorporation into a model. This model exhibited an area under the ROC curve value of 0.9147 indicating the potential application of this approach for rapidly characterising MRSA and MSSA isolates. Nearly all strains ( n = 97) were correctly assigned to the correct group, with only two aberrant MSSA strains being misclassified. However, approximately 21% of the strains appeared to be in a process of transition as resistance to methicillin was being acquired.</abstract><cop>Germany</cop><pub>Elsevier GmbH</pub><pmid>21257279</pmid><doi>10.1016/j.syapm.2010.11.002</doi><tpages>6</tpages></addata></record>
fulltext fulltext
identifier ISSN: 0723-2020
ispartof Systematic and applied microbiology, 2011-02, Vol.34 (1), p.81-86
issn 0723-2020
1618-0984
language eng
recordid cdi_proquest_miscellaneous_907152616
source MEDLINE; Elsevier ScienceDirect Journals Complete
subjects ANN
Bacterial Proteins - analysis
CM10
Drug Resistance, Bacterial
Methicillin-Resistant Staphylococcus aureus - chemistry
Methicillin-Resistant Staphylococcus aureus - classification
MRSA
MSSA
Neural Networks (Computer)
Protein Array Analysis - methods
SELDI-TOF-MS
Sensitivity and Specificity
Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization - methods
Staphylococcus aureus
title Tracing the transition of methicillin resistance in sub-populations of Staphylococcus aureus, using SELDI-TOF Mass Spectrometry and Artificial Neural Network Analysis
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-07T15%3A58%3A57IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Tracing%20the%20transition%20of%20methicillin%20resistance%20in%20sub-populations%20of%20Staphylococcus%20aureus,%20using%20SELDI-TOF%20Mass%20Spectrometry%20and%20Artificial%20Neural%20Network%20Analysis&rft.jtitle=Systematic%20and%20applied%20microbiology&rft.au=Shah,%20Haroun%20N.&rft.date=2011-02-01&rft.volume=34&rft.issue=1&rft.spage=81&rft.epage=86&rft.pages=81-86&rft.issn=0723-2020&rft.eissn=1618-0984&rft_id=info:doi/10.1016/j.syapm.2010.11.002&rft_dat=%3Cproquest_cross%3E853225957%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=853225957&rft_id=info:pmid/21257279&rft_els_id=S0723202010001645&rfr_iscdi=true